{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "start_time": "2023-07-08T12:54:18.370882Z",
     "end_time": "2023-07-08T12:56:58.391969Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\PCL\\PyCharm 2023.1\\jbr\\bin\\D\\code\\env\\lib\\site-packages\\gym\\envs\\registration.py:593: UserWarning: \u001B[33mWARN: The environment CartPole-v0 is out of date. You should consider upgrading to version `v1`.\u001B[0m\n",
      "  logger.warn(\n",
      "E:\\PCL\\PyCharm 2023.1\\jbr\\bin\\D\\code\\env\\lib\\site-packages\\gym\\core.py:317: DeprecationWarning: \u001B[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001B[0m\n",
      "  deprecation(\n",
      "E:\\PCL\\PyCharm 2023.1\\jbr\\bin\\D\\code\\env\\lib\\site-packages\\gym\\wrappers\\step_api_compatibility.py:39: DeprecationWarning: \u001B[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001B[0m\n",
      "  deprecation(\n",
      "E:\\PCL\\PyCharm 2023.1\\jbr\\bin\\D\\code\\env\\lib\\site-packages\\gym\\core.py:256: DeprecationWarning: \u001B[33mWARN: Function `env.seed(seed)` is marked as deprecated and will be removed in the future. Please use `env.reset(seed=seed)` instead.\u001B[0m\n",
      "  deprecation(\n",
      "Iteration 0:   0%|          | 0/50 [00:00<?, ?it/s]C:\\Users\\14227\\AppData\\Local\\Temp\\ipykernel_11764\\3199113730.py:49: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ..\\torch\\csrc\\utils\\tensor_new.cpp:248.)\n",
      "  state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
      "E:\\PCL\\PyCharm 2023.1\\jbr\\bin\\D\\code\\env\\lib\\site-packages\\gym\\utils\\passive_env_checker.py:241: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
      "  if not isinstance(terminated, (bool, np.bool8)):\n",
      "Iteration 0: 100%|██████████| 50/50 [00:13<00:00,  3.64it/s, episode=50, return=140.100]\n",
      "Iteration 1: 100%|██████████| 50/50 [00:16<00:00,  2.98it/s, episode=100, return=200.000]\n",
      "Iteration 2: 100%|██████████| 50/50 [00:17<00:00,  2.82it/s, episode=150, return=186.400]\n",
      "Iteration 3: 100%|██████████| 50/50 [00:17<00:00,  2.89it/s, episode=200, return=200.000]\n",
      "Iteration 4: 100%|██████████| 50/50 [00:13<00:00,  3.83it/s, episode=250, return=200.000]\n",
      "Iteration 5: 100%|██████████| 50/50 [00:13<00:00,  3.81it/s, episode=300, return=200.000]\n",
      "Iteration 6: 100%|██████████| 50/50 [00:13<00:00,  3.69it/s, episode=350, return=188.700]\n",
      "Iteration 7: 100%|██████████| 50/50 [00:13<00:00,  3.75it/s, episode=400, return=199.200]\n",
      "Iteration 8: 100%|██████████| 50/50 [00:21<00:00,  2.37it/s, episode=450, return=186.000]\n",
      "Iteration 9: 100%|██████████| 50/50 [00:16<00:00,  2.98it/s, episode=500, return=200.000]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import gym\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import rl_utils\n",
    "\n",
    "\n",
    "class PolicyNet(torch.nn.Module):\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super(PolicyNet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        return F.softmax(self.fc2(x), dim=1)\n",
    "\n",
    "\n",
    "class ValueNet(torch.nn.Module):\n",
    "    def __init__(self, state_dim, hidden_dim):\n",
    "        super(ValueNet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        return self.fc2(x)\n",
    "\n",
    "\n",
    "class PPO:\n",
    "    ''' PPO算法,采用截断方式 '''\n",
    "\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr,\n",
    "                 lmbda, epochs, eps, gamma, device):\n",
    "        self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)\n",
    "        self.critic = ValueNet(state_dim, hidden_dim).to(device)\n",
    "        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),\n",
    "                                                lr=actor_lr)\n",
    "        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),\n",
    "                                                 lr=critic_lr)\n",
    "        self.gamma = gamma\n",
    "        self.lmbda = lmbda\n",
    "        self.epochs = epochs  # 一条序列的数据用来训练轮数\n",
    "        self.eps = eps  # PPO中截断范围的参数\n",
    "        self.device = device\n",
    "\n",
    "    def take_action(self, state):\n",
    "        state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
    "        probs = self.actor(state)\n",
    "        action_dist = torch.distributions.Categorical(probs)\n",
    "        action = action_dist.sample()\n",
    "        return action.item()\n",
    "\n",
    "    def update(self, transition_dict):\n",
    "        states = torch.tensor(transition_dict['states'],\n",
    "                              dtype=torch.float).to(self.device)\n",
    "        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(\n",
    "            self.device)\n",
    "        rewards = torch.tensor(transition_dict['rewards'],\n",
    "                               dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        next_states = torch.tensor(transition_dict['next_states'],\n",
    "                                   dtype=torch.float).to(self.device)\n",
    "        dones = torch.tensor(transition_dict['dones'],\n",
    "                             dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        td_target = rewards + self.gamma * self.critic(next_states) * (1 -\n",
    "                                                                       dones)\n",
    "        td_delta = td_target - self.critic(states)\n",
    "        advantage = rl_utils.compute_advantage(self.gamma, self.lmbda,\n",
    "                                               td_delta.cpu()).to(self.device)\n",
    "        old_log_probs = torch.log(self.actor(states).gather(1,\n",
    "                                                            actions)).detach()\n",
    "\n",
    "        for _ in range(self.epochs):\n",
    "            log_probs = torch.log(self.actor(states).gather(1, actions))\n",
    "            ratio = torch.exp(log_probs - old_log_probs)\n",
    "            surr1 = ratio * advantage\n",
    "            surr2 = torch.clamp(ratio, 1 - self.eps,\n",
    "                                1 + self.eps) * advantage  # 截断\n",
    "            actor_loss = torch.mean(-torch.min(surr1, surr2))  # PPO损失函数\n",
    "            critic_loss = torch.mean(\n",
    "                F.mse_loss(self.critic(states), td_target.detach()))\n",
    "            self.actor_optimizer.zero_grad()\n",
    "            self.critic_optimizer.zero_grad()\n",
    "            actor_loss.backward()\n",
    "            critic_loss.backward()\n",
    "            self.actor_optimizer.step()\n",
    "            self.critic_optimizer.step()\n",
    "\n",
    "\n",
    "actor_lr = 1e-3\n",
    "critic_lr = 1e-2\n",
    "num_episodes = 500\n",
    "hidden_dim = 128\n",
    "gamma = 0.98\n",
    "lmbda = 0.95\n",
    "epochs = 10\n",
    "eps = 0.2\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\n",
    "    \"cpu\")\n",
    "\n",
    "env_name = 'CartPole-v0'\n",
    "env = gym.make(env_name)\n",
    "env.seed(0)\n",
    "torch.manual_seed(0)\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = env.action_space.n\n",
    "agent = PPO(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda,\n",
    "            epochs, eps, gamma, device)\n",
    "\n",
    "return_list = rl_utils.train_on_policy_agent(env, agent, num_episodes)\n",
    "episodes_list = list(range(len(return_list)))\n",
    "plt.plot(episodes_list, return_list)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('PPO on {}'.format(env_name))\n",
    "plt.show()\n",
    "\n",
    "mv_return = rl_utils.moving_average(return_list, 9)\n",
    "plt.plot(episodes_list, mv_return)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('PPO on {}'.format(env_name))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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